Various embodiments associated with an interface that displays a visualization are described. In a combat or other environment, a great deal of information can be made available. In addition, this information can be compared against a metric of performance. This metric of performance could be qualitative or quantitative. In a complex environment, a relatively large amount of information can be gathered and it can be beneficial to produce an interface that enables a user to quickly understand the information with specific emphasis on how an entity related to the information is performing against the metric. The interface can include various features, such as time controls that allows for playback of previous information, measures of performance, and a structured tree view that illustrates hierarchical relationships of elements.
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5. A system, comprising an adaptive learning system, the adaptive learning system being configured to:
become trained;
be deployed after being trained; and
be employed in making a prediction of for a multi-leveled organization such that a result of the prediction in multiple levels of the organization with relation to a physical spatial layout representation is illustrated.
1. A system, comprising:
a trainer component configured to train an adaptive learning system;
a deployment component configured to cause the adaptive learning system to be deployed after the adaptive learning system is successfully trained;
a prediction component configured to make a prediction for a multi-tiered organization through employment of the deployed adaptive learning system; and
a visualization component configured to illustrate a result of the prediction in multiple tiers of the organization with relation to a physical spatial layout representation.
15. A non-transitory computer-readable medium configured to store processor-executable instructions that when executed by a processor cause the processor to perform a method, the method comprising:
training an adaptive learning system;
causing deployment of the adaptive learning system after the adaptive learning system is successfully trained;
making a prediction for a multi-tiered organization through employment of the deployed adaptive learning system; and
causing illustration of a result from the prediction in multiple tiers of the organization with relation to a physical spatial layout representation.
2. The system of
where the adaptive learning system is configured to determine operational performance of the organization through use of a scoring system.
3. The system of
an interface component configured cause the illustration of the result to be displayed upon an interface
where the prediction is based on a scenario.
4. The system of
6. The system of
observe an outcome associated with the prediction;
make a comparison of the outcome against the prediction;
make a first determination on if the trained intelligence should be modified based, at least in part, on a result of the comparison; and
make a second determination on a modification for the trained intelligence in response to the first determination being that the trained intelligence should be modified,
where the modification is based, at least in part, on a result of the comparison and
where the trained intelligence is modified in accordance with the modification.
7. The system of
where the prediction comprises a prediction of a measure of performance of the organization.
8. The system of
where the prediction comprises a prediction of an operational result of the organization.
9. The system of
where the adaptive learning system is configured to be trained while deployed.
10. The system of
a processor configured to execute at least one instruction associated with the trainer component, the deployment component, the prediction component, the visualization component, or a combination thereof.
11. The system of
where the adaptive learning system is configured to be self-trained after being deployed and be trained in a manner other than self-training before being deployed.
14. The system of
where the prediction is on a measure of performance of a lower level of the organization and
where the prediction is on a measure of performance of a higher level of the organization.
16. The non-transitory computer-readable medium of
where the adaptive learning system is configured to determine an effectiveness of an organization once deployed.
17. The non-transitory computer-readable medium of
where the prediction pertains to a highest tier of the multi-tiered organization and
where the prediction pertains to a lowest tier of the multi-tiered organization.
18. The non-transitory computer-readable medium of
where the adaptive learning system is configured to determine operational performance of the organization through use of a scoring system.
19. The non-transitory computer-readable medium of
where the adaptive learning system is configured to be further trained after deployment.
20. The non-transitory computer-readable medium of
where the illustration is with relation to a temporal representation concurrently with the physical spatial layout representation.
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This application is a divisional application of, and claims priority to, U.S. application Ser. No. 13/803,812. The entirety of U.S. application Ser. No. 13/803,812 is incorporated by reference.
The innovation described herein may be manufactured, used, imported, sold, and licensed by or for the Government of the United States of America without the payment of any royalty thereon or therefore.
In a combat environment, a wide range of information can be made available. For example, a combat force can include an armor group, an infantry group, and an artillery group. Information can be produced from the armor group about how individual tanks are performing and how the tanks as a group are performing. Similar information can be produced for the infantry group and artillery group. Furthermore, information can be gathered on how the different groups interact with one another such as how infantry soldiers perform after the artillery group completes a task.
A method is disclosed comprising accessing, by way of a visualization apparatus, a performance metric of a system comprising a distributed database. The method also comprises accessing, by way of the visualization apparatus, a performance threshold for the performance metric, where the performance metric is derived from a first verification and evaluation subsystem that operates in parallel to a second verification and evaluation subsystem. The method further comprises causing, by way of the visualization apparatus, a first visualization to display on an interface, where the first visualization illustrates the performance metric against the performance threshold and where the performance threshold is adjustable by way of the interface while the visualization is displayed. The method additionally comprises causing, by way of the visualization apparatus, the first visualization to be replaced with a second visualization, where the first visualization is of a first level of the system, where the second visualization is of a second level of the system, where the first level and the second level are time-synchronized with one another, and where the first level and the second level share a hierarchical relationship.
A system is disclosed comprising a trainer component, a deployment component, and a processor. The trainer component is configured to train an adaptive learning system through use a correlation of technical performance data against operational performance data, where the adaptive learning system is configured to predict operational performance of a system from a technical perspective and from a tiered data and metric perspective. The deployment component is configured to cause the adaptive learning system to be deployed after the adaptive learning system is trained, where the adaptive learning system is configured to determine an effectiveness of an organization once deployed. The processor is configured to execute at least one instruction associated with the trainer component or the deployment component.
A system comprising an adaptive learning system is described. The adaptive learning system is configured to receive a set of technical performance data from an organization. The adaptive learning system is also configured to make a prediction of the organization based, at least in part, on the technical performance data, where the prediction is made through use of a trained intelligence, where the prediction is accessed by a visualization and analysis subsystem, and where the visualization and analysis subsystem causes the prediction to be represented in a visualization that is presented on an interface.
Incorporated herein are drawings that constitute a part of the specification and illustrate embodiments of the detailed description. The detailed description will now be described further with reference to the accompanying drawings as follows:
Aspects related to correlation of technical performance data against operational performance data are described. In a combat or other environment, a great deal of information can be made available. In addition, this information can be compared against a metric of performance (e.g., one or more performance metric). This metric of performance could be qualitative or quantitative. In a complex environment, a relatively large amount of information can be gathered and it can be beneficial to produce an interface that enables a user to quickly understand the information with specific emphasis on how an entity related to the information is performing against the metric.
The interface can include a graphic that shows how information, and thus how an underlying aspect, measures against a performance metric. A visual indicator can be provided on how the information measures against the performance metric. In one example, if the metric is being met with high success, then the visual indicator can be green. If the metric is being met with passing, yet not high, success, then the visual indicator can be yellow. If the metric is not being met, then the visual indicator can be red. The visual indicator can be a real-time representation of performance of an aspect and the performance metric can be modifiable.
Various features disclosed herein can be practiced and visualized. One feature can be making a correlation of operation and technical and requirements decomposition with subsequent proactive prediction of operational from technical data. Another feature can be distributed computation of various layer measures of performance. Yet another feature can be proactive determination of requirements satisfaction (e.g., operational or technical at various layers of a system or force). A further feature is control of analytical and experimentation scope.
The following includes definitions of selected terms employed herein. The definitions include various examples. The examples are not intended to be limiting.
“One embodiment”, “an embodiment”, “one example”, “an example”, and so on, indicate that the embodiment(s) or example(s) can include a particular feature, structure, characteristic, property, or element, but that not every embodiment or example necessarily includes that particular feature, structure, characteristic, property or element. Furthermore, repeated use of the phrase “in one embodiment” may or may not refer to the same embodiment.
“Computer-readable medium”, as used herein, refers to a medium that stores signals, instructions and/or data. Examples of a computer-readable medium include, but are not limited to, non-volatile media and volatile media. Non-volatile media may include, for example, optical disks, magnetic disks, and so on. Volatile media may include, for example, semiconductor memories, dynamic memory, and so on. Common forms of a computer-readable medium may include, but are not limited to, a floppy disk, a flexible disk, a hard disk, a magnetic tape, other magnetic medium, other optical medium, a Random Access Memory (RAM), a Read-Only Memory (ROM), a memory chip or card, a memory stick, and other media from which a computer, a processor or other electronic device can read. In one embodiment, the computer-readable medium is a non-transitory computer-readable medium.
“Component”, as used herein, includes but is not limited to hardware, firmware, software stored on a computer-readable medium or in execution on a machine, and/or combinations of each to perform a function(s) or an action(s), and/or to cause a function or action from another component, method, and/or system. Component may include a software controlled microprocessor, a discrete component, an analog circuit, a digital circuit, a programmed logic device, a memory device containing instructions, and so on. Where multiple components are described, it may be possible to incorporate the multiple components into one physical component or conversely, where a single component is described, it may be possible to distribute that single logical component between multiple components.
“Software”, as used herein, includes but is not limited to, one or more executable instructions stored on a computer-readable medium that cause a computer, processor, or other electronic device to perform functions, actions and/or behave in a desired manner. The instructions may be embodied in various forms including routines, algorithms, modules, methods, threads, and/or programs including separate applications or code from dynamically linked libraries.
The VAS can proactively (e.g., automatically) produce visual identification of the threshold vs. objective requirement violations and visualization of the instrumented data 130 (e.g., in addition to other data). The VAS can comprise a client subsystem and a server subsystem. The server subsystem can interact with multiple client subsystems such that individual client subsystems are kept independent. In addition, a plurality of VASs 110 can facilitate analysis scalability. The VAS 110 and/or the VES 120 can build compound metrics from data about the system (e.g., an element of the system). In one embodiment, the VES 120 can verify requirements on top of other functions while the VAS 110 provides visualization aspects and provides functionality for experiment control selection as well as scope selection. In one embodiment, the VES 120 produces metadata pertaining to MOP requirement satisfaction (e.g., at a global requirement level). A requirement changed by a user can be assessed and rendered by the VAS 110. The VAS 110 can render data and thus create a visualization disclosed herein as well as render requirement satisfaction metadata produced by the VES 120 (e.g., satisfaction metadata is disclosed as part of the visualization).
The adaptive learning system 140 can be trained using technical performance data that is part of the instrumented data, operational performance data (e.g., instrumented data, surveyed data, etc.), or other data. This training can occur while the instrumented data is correlated, by a component, with operational performance data and/or overall operational capability assessment. The adaptive learning system 140 can include human-in-loop upon its training to determine operational effectiveness scoring of network components (e.g., component of a network from which the instrumented data 130 is obtained). When deployed, the adaptive learning system 140 can proactively determine operational performance given technical performance data (e.g., that is part of the instrumented data) from the network's instrumentation. The adaptive learning system 140 (e.g., once initially training and deployed) can predict operational performance from technical performance. The adaptive learning system 140 can provide results from analyses that are stored in the distributed database 150 and the distributed database 150 makes these results available to the VAS 110.
In one embodiment, the distributed database 150 of
In one embodiment, a map with element 210 (e.g., map displaying one or more elements with a visual indicator of actual performance against threshold, such as a numeric value with a specific color) can be part of the interface that shows a physical location of the tanks and cannons (e.g., along a terrain map derived from an Internet-based map application) along with performance data. The visualization can include an icon that represents the tanks and cannons, as well as listing performance data. Further, the performance data that is listed and/or an icon (e.g., an icon of a tank) can be color-coded based on how the data measures against a performance metric (e.g., green for pass and red for fail).
Additionally, the interface 200 can include various visual representations of MOPs 220a-220c. The MOPs 220a-220c can vary in complexity, and include items such as graphs (e.g., transport network architecture graphs), threads, depictions (e.g., individual software service depictions), etc. A viewer of the interface 200 can select what is contained in the MOPs 220a-220c. In one example, the viewer can designate MOP 220a as ammunition usage rate of the two cannons combined, MOP 220b can be fuel rate consumption of the second tank, and MOP 220c can be firing accuracy of the overall force. While three MOPs 220a-220c are illustrated, it is to be appreciated by one of ordinary skill in the art that the interface 200 can comprise more or less than three MOPs. In one embodiment, the adaptive learning system 140 of
The interface 200 (e.g., graphical user interface) can also include time controls 230 such as pause, rewind, play, fast forward, and record. Data pertaining to the force can be recorded (e.g., proactively, after a viewer designates recording to occur, etc.) and the viewer can view the map with element 210 and/or at least one of the MOPs 220a-220c in real-time and/or from a previous time. The time controls 230 can include buttons (e.g., buttons can that can be pressed on a touch screen or through use of a computer mouse) and/or be graphically similar to those of a Digital Video Recorder (DVR).
The interface 200 can further include a scope selection 240. Different scopes can be selected that define other parts of the interface 200 and/or that limit viewing to a specific scope. For example, scope can be selected from choices of services (e.g., mission command applications and services), entities, threads, or transports. Other scope choices can be selected from among different transport networks, different ammunition systems, threads of exchanges (e.g., message exchanges, logical exchanges, etc.) between force entities, etc.
In addition, the interface 200 can include a structured tree view 250. The structured tree view 250 can be a hierarchical menu of the force. In the above example, the force can have a first level folder. Under the first level folder can be two second level folders—one for the armor section and one for the artillery section. Under armor section folder three third level folders can exist for individual tanks and under an individual tank folder four fourth level folders can exist for individual soldiers. Likewise, under the artillery section folder three third level folders can exist for individual cannons and under an individual cannon folder three fourth level folders can exist for individual soldiers. The structured tree view 250 can be color coded based on performance. For example, if tank two out of three is not firing accurately against a metric, then the tank two folder, the four individual soldier folders under the tank two folder, the tank section folder, and the force folder can be colored red to indicate a requirement for the metric not being met while remaining folders are colored green. In one embodiment, the four individual soldier folders can be yellow indicating that they are not failing, but associated with a tank that is failing. Thus, the structured tree view 250 can indicate where a failure is occurring. Further, selecting a folder of the structured tree view 250 can cause default information to be displayed in the map with element 210 and/or cause specific information to be represented in the MOPs 220a-220c.
Different levels of the structured tree view 250 can present different information. In one example, at a first level different platforms can be selected, at a second level different mission command applications can be selected, and at a third level different software services or message types can be selected.
In addition, the interface 200 can be used to control parameterization of modeling and simulation, scenarios on top of scope selection, and others. In one example, the interface 200 can include a keyboard (e.g., an on-screen keyboard) or be connected to a keyboard (e.g., and mouse). Through use of the keyboard, a view of the interface can customize parameters, use the time controls 230, etc.
While aspects disclosed herein discuss a single force of two sections and five elements, it is to be appreciated by one of ordinary skill in the art that aspects disclosed herein can be practiced with regard to more simple or more complex arrangements as well as smaller or larger arrangements (e.g., division-sized forces and systems of systems representing those forces). While aspects described herein are discussed as relating to a combat environment, it is to be appreciated by one of ordinary skill in the art that aspects disclosed herein can be practiced in non-combat environments. For example, aspects disclosed herein can be used to manage business performance (e.g., how individuals are performing at their computers, how office sites are performing, etc.), observe sports performance (e.g., for an auto racing team fielding several cars in a race, how individual cars are performing, how parts of the car are performing, etc.), and others.
The system 300 can construct the adaptive learning system 140 and the trainer component 310 can present the adaptive learning system 140 with various input/output dependencies and correlated data sets. The adaptive learning system 140 can explore the correlations between these inputs and outputs against a training set of data (e.g., data obtained from the distributed database 150 of
In one embodiment, the adaptive learning system 140 is configured to receive a set of technical performance data (e.g., fire accuracy) from an organization (e.g., the force). The adaptive learning system 140 is also configured to make a prediction based, at least in part, on the technical performance data, where the prediction is made through use of trained intelligence (e.g., neural network trained by the trainer component 310), where the prediction is accessed by the VAS 110 of
Using the previous example, the trainer component 310 can train the adaptive learning system 140 such that when the tanks have a fire accuracy (e.g., technical performance data) of x %, the adaptive learning system 140 can predict that the mission has y % chance of eliminating a target (e.g., operational performance data) and target elimination can be operational performance. Thus, when the fire accuracy of the tanks (e.g., average among the tanks) is at x %, target elimination likelihood can be at y %. The adaptive learning system 140 can predict that if tanks have a fire accuracy of x % then target elimination is likely to occur at y % (e.g., technical perspective) and that if target elimination is likely to occur at y % from the tanks, then the force has a z % chance of mission completeness (e.g., a tiered data and metric perspective). In addition, the trainer component 310 can train the adaptive learning system 140 to make predictions on how the artillery performs in response to performance of the tanks. For example, accuracy of the tanks can be based on wind and this wind can influence accuracy of the artillery. Thus, a correlation can be drawn that low accuracy of the tanks in high wind could lead to low accuracy of artillery, and as such, a determination can be made that the artillery will have low accuracy and thus the force will have unacceptable accuracy. The deployment component 320 can deploy the adaptive learning system 140 with an operation. During the operation, fire accuracy can be monitored and based on a result from this monitoring, the adaptive learning system 140 can determine an effectiveness (e.g., target elimination likelihood based on fire rate accuracy) of the tanks and/or the force. While this is a relatively simple example, it is to be appreciated by one of ordinary skill in the art that more complex correlations can be made with more available data for examples disclosed herein.
In one embodiment, the adaptive learning system 140 is configured to determine operational performance of the organization through use of a scoring system (e.g., according to a set of defined compound-type MOPs). In one example, tank accuracy can be given a first weighted score and artillery accuracy can be given a second weighted score. The weighted scores can be combined and operational performance can be given a classification (e.g., exceptional, good, passing, failure) based on the combined score and/or individual scores (e.g., exceptional is given if the combined score meets an exceptional threshold and the tank and artillery scores meet individual exceptional thresholds).
In one embodiment, the technical performance data is proactively (e.g., automatically) determined (e.g., proactively determined by the VAS 110 of
In one embodiment, the trainer component 310 is configured to train the adaptive learning system 140 after deployment and/or the adaptive learning system 140 can be developed such that the adaptive learning system 140 is self-trainable. While the trainer component 310 can initially train the adaptive learning system 140, more information can become available that can be used to better train the adaptive learning system 140, information can change, etc. Therefore, after deployment the trainer component 310 can train (e.g., re-train, update, etc.) the adaptive learning system 140 and/or the adaptive learning system 140 can train itself.
In one embodiment, the adaptive learning system 140 can be self-trainable (e.g., be further trained) after deployment. In one example, the adaptive learning system 140 can be configured to observe an outcome associated with the prediction and make a comparison of the outcome against the prediction The adaptive learning system 140 can also be configured to make a first determination on whether the trained intelligence should be modified based, at least in part, on a result of the comparison as well as make a second determination on a modification for the trained intelligence in response to the first determination being that the trained intelligence should be modified. The adaptive learning system 140 can modify the trained intelligence in accordance with the modification that is based, at least in part, on a result of the comparison and the trained intelligence.
In one embodiment, the organization is a first organization (e.g., tanks), where the adaptive learning system 140 is configured to determine an effectiveness of a second organization (e.g., artillery), once deployed concurrently with determination of the effectiveness of the first organization (e.g., the first organization and second organization are sub-organizations of a third organization (e.g., the force), the first organization and the second organization are separate and distinct organizations, etc.). In one example, the adaptive learning system 140 can determine effectiveness of the force as well as effectiveness of the tanks, an individual tank, and parts of an individual tank (e.g., firing mechanism, driver, etc.). Thus, different levels of hierarchy can have their effectiveness determined. This effectiveness can be independent (e.g., the tanks as a group can be deemed effective while an individual tank is deemed ineffective) or dependent (e.g., the tanks as a group are not deemed effective unless the individual tanks of the group are individually deemed effective, the tanks as a group are not deemed effective unless the individual tanks of the group are individually deemed effective and the group is deemed effective, etc.).
In one embodiment, a request can be made on a likely result of an operation (e.g., user-generated request, computer-generated request, etc.). In one example, a commander can request how successful the force would be in elimination of a structure. The prediction component 410 can process this request and cause the adaptive learning system 140 to run a simulation on how the force would do based on weather conditions, training level of soldiers, etc. Running this simulation can cause prediction of a success of elimination of the structure (e.g., a percentage likelihood of success, a yes or no on if success is likely, a detailed prediction that details equipment damage, supply usage and other information, etc.). In one embodiment, a user or component enters the scenario and the prediction component 410 provides suggestions to the scenario that can improve success or change a certain factor, such as supply usage (e.g., taking a different path than requested, suggesting a best path when a path is not specified in the request, etc.). In one embodiment, the adaptive learning system 140 can predict elimination of a structure based, at least in part, on metric computation (e.g., lower level metric computation such as supply usage, health of an individual tank battle command system, or maneuverability against at least one battle scenario trigger). In one example, the adaptive learning system 140 can predict a higher level MOP from at least one lower level MOP (e.g., predict firing accuracy of the force from firing accuracy of a particular cannon, predict firing accuracy of the tanks from firing accuracy of the particular cannon, predict firing accuracy of the artillery group from firing accuracy of the particular cannon, predict tank ammunition usage from firing accuracy of the particular cannon, etc.). In one example, sensor feeds can be predicted from lower level atomic MOPs that indicate message completion of individual video data packets.
In addition to correlations being produced (e.g., observed, drawn, etc.) by the correlation component 510 to initially train the adaptive learning system 140, the adaptive learning system 140 can produce its own correlations (e.g., once deployed) and use those correlations to further train. These correlations can be based, at least in part, on the manually entered information set, an addition to the instrumented data 130, an observation made by the adaptive learning system 140, etc.
In one embodiment, the adaptive learning system 140 can be trained both through use of instrument-based data and user-entered data. In one example, the correlation can be when cannon accuracy is at a %, then cannon ammunition usage is at b %. In addition, a user can enter that when cannon accuracy is at a %, soldier fatigue increases by c %. With this information, trainer component 310 can train the adaptive learning system 140 in this example. The adaptive learning system 140 can be trainable using cannon accuracy, cannon ammunition usage, and at least one MOP or survey provided (e.g., computed) by a VAS 110 of
In one embodiment, the system 600 can comprise other components. In one example, the system 600 can comprise a capture component that captures a qualitative performance metric (e.g., one or more qualitative performance metric) of an organization and a quantitative performance metric (e.g., one or more quantitative performance metric) of the organization. The system 600 can also comprise a cross-correlation component that makes a cross-correlation between the qualitative performance metric and the quantitative performance metric, where the cross-correlation is disclosed. In one example, the system 600 further comprises a determination component that makes a determination of mission effectiveness based, at least in part, on the cross-correlation.
In one example, the qualitative performance metric can be accuracy from shells shot from the artillery of the previous example. The quantitative performance metric can be an amount of ammunition used per period of time. The cross-correlation can be that as more ammunition is used, accuracy is reduced. The determination of mission effectiveness can be that using n number of rounds per t amount of time will give g % of accuracy. When g % accuracy is reached a goal is achieved. In one embodiment, the adaptive learning system 140 can use correlations to establish dependencies between at least one input (e.g., atomic MOP) and at least one output (e.g., operational MOP, higher level MOP, etc.) upon training.
Mission effectiveness can be how successfully a goal is achieved. The goal can be an objective goal or a subjective goal. The performance metric and/or the performance threshold can be objective (e.g., unit of measure for the force) or subjective (e.g., a commander's pleasure with performance).
In one example, the replacement can cause for data about a higher level of the organization (e.g., firing accuracy of the force) to be replaced with data about a lower level of the organization (e.g., firing accuracy of an individual tank) through engaging an icon on the interface in the tree stricture representing the lower level or vice versa (e.g., lower level to higher) as well as parallel and others (e.g., from a level on one branch to a higher, lower, or equal level on another branch). A map of the visualization can remain the same, but elements shown on the map can be changed (e.g., added, removed, modified, etc.) from the first visualization to the second visualization.
Returning to the previous example, a computer associated with the force can access a performance metric of the force (e.g., an amount of ammunition actually used by the force per hour while engaged in combat) and a performance threshold of the force (e.g., how much ammunition the force should be using while engaged in combat). The interface can display an MOP that shows the actual ammunition usage against a desired ammunition usage. In one example, the MOP can show that the actual ammunition usage is less than the desired ammunition of the force. A viewer of the interface can desire to find out what part of the force is not using the desired amount of ammunition and therefore send a request for an MOP of ammunition usage of the tanks to be presented. The MOP of the ammunition usage of the tanks can show that actual ammunition usage of the tanks is consistent with desired ammunition usage. Thus, the user can desire to view an MOP of the artillery. The MOP of the tanks can be replaced with an MOP of the artillery (e.g., direct replacement, return to MOP of the force then present the MOP of the artillery, etc.). The MOP of the artillery (e.g., a graph plotting usage per time period against desired usage) can show that ammunition artillery usage is not desired. The view can go further down into MOPs of individual cannons to isolate a problem and cause a command to be directed to change behavior (e.g., send an instruction to a personal electronic device of a soldier operating a cannon to change an ammunition usage rate).
In one embodiment, an individual VAS 110 of
In one embodiment, factors can change and this can be a reason for the performance threshold to change. For example, a first ammunition rate can be desired. However, a resupply of ammunition can be delayed. This delay may cause the force to run out of ammunition. To avoid this situation, the desired rate of usage can be decreased (e.g., automatically, through explicit user instruction, etc.). The MOPs discussed in the last paragraph can be modified to compare actual usage against the new desired usage.
In one embodiment, the interface facilitates adjustment of the visualization according to scope (e.g., through use of the scope selection 240 of
In one embodiment, the performance threshold comprises multiple levels. For example, the performance threshold can comprise a performance objective level (e.g., a desired level that causes an element of the visualization to be colored green), a performance threshold level (e.g., a minimally passing level that causes an element of the visualization to be colored yellow such that a threshold requirement is met but not an objective requirement), and a performance non-compliant level (e.g., an unacceptable level that causes an element of the visualization to be colored red).
In one embodiment, the visualization is displayed, at least in part, integrated with a spatial (e.g., physical distance) and temporal (e.g., time-based) system layout representation against multiple environments (e.g., terrain environment, weather environment, infrared environment, etc.). In one embodiment, the performance metric is a real-time metric (e.g., performance in real-time). The visualization can change as the performance metric changes to reflect a recent status of the performance metric against the performance threshold (e.g., if an individual tank's fire accuracy decreases over time, then a color of an element of the visualization can change from green to yellow if appropriate).
In one embodiment, the first visualization comprises a structured tree view (e.g., structured tree view 250 of
In one embodiment, the visualization comprises a structured tree portion (e.g., structured tree view 250) and an integrated selection portion, where the integrated selection portion comprises an experimentation selection portion that, when engaged, enables entry of an experimentation construct (e.g., a hypothetical scenario that can be run on how the force will achieve an objective); a subsystem selection portion that, when engaged, enables focus of the visualization on a selected subsystem of the system; and a parameter selection portion that, when engaged, enables selection of a parameter of at least part of the system (e.g., set maximum speed of a tank).
In one embodiment, multiple editions of an initial visualization are populated upon a plurality of interfaces. Individual viewers of the interfaces can manipulate the visualization to their desire. Example manipulations can include changing MOPs displayed, changing map zoom level, changing scope, using playback functionality, etc. In one embodiment, different visualizations are initially created that are manipulatable.
In one embodiment, the performance threshold of the visualization displayed at the first interface is changed to a first changed performance threshold and the visualization displayed at the first interface is changed to reflect the first changed performance threshold. In one example, at the first interface the performance threshold is raised and in response a MOP of the visualization at the first interface is modified accordingly. The performance threshold of the visualization displayed at the second interface is changed to a second changed performance threshold and the visualization displayed at the second interface is changed to reflect the second changed performance threshold. In one example, at the second interface the performance threshold is lowered and in response a MOP of the visualization in the second interface is modified accordingly. Thus, the first changed performance threshold and the second changed performance threshold can be different. The visualization can be changed according to the first changed performance threshold and can be displayed on the first interface concurrently with display on the second interface of the visualization changed according to the second changed performance threshold.
In one embodiment, the performance metric is obtained, at least in part, through measurement of real-time performance of the system. In one embodiment, the performance metric is, at least in part, a performance experimentation record of the system and the performance metric is obtained from a computer-readable medium separable from the distributed database. In one embodiment, the performance metric is, at least in part, a result from a simulation of the system, where the simulation is run by a component separate from the system.
As the instrumented data 130 of
In one embodiment, returning to the previous example, actual fuel consumption of an individual tank (the performance metric) can be compared against a desired consumption of fuel for the individual tank (the performance threshold). The result of this comparison (e.g., functioning as the analysis) can be that the fuel consumption is greater than desired and thus considered failing (e.g., the designation of failing can function as the determination). This failure can be presented on the visualization as showing a name of the tank on a map in red. An identification can occur (e.g., proactively, in response to a user request, when the failure meets an additional threshold, etc.) that identifies why the failure occurs and a reason for the failure can be displayed. In one example, a diagnostic check can be performed on a fuel system of the individual tank and a determination can be made that a specific situation (e.g., fuel-line leak, a corrupted computer fuel management system, incorrect throttle use by a soldier, etc.) causes the failure. This failure can be logged (e.g., in the distributed database 150 of
Bushmitch, Dennis, Badger, Michael, Cozby, Richard
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